MATHEMATICAL MODELING AND INFERENCE OF CANCER NETWORKS
Abstract
Cancer is a group of diseases characterized by abnormal cell growth. Old cells do not die and grow uncontrollably, forming a mass of tissue, called a tumor. In order to understand this abnormal cell growth, there have been various efforts to model the interactions between different molecules and pathways that initiate and drive cell proliferation. In this work, we analyze Bayesian and Boolean techniques that can aid in modeling different cancer networks and infer the drug combinations that can effectively kill tumor cells.
Signaling pathways supervise cellular processes such as growth, differentiation, and death. In healthy cells, these processes are tightly regulated, however, in cancerous cells, mutations in crucial genes often lead to irregularities in these processes and eventually cancer. In this work, we study pathways and genes characterizing Breast cancer, Pancreatic cancer, and Lung cancer. We make use of biological literature to construct the pathways and then use mathematical modeling techniques to analyze and rank different therapeutic interventions. We first develop a Bayesian network of Breast cancer and using a messaging passing algorithm, we infer the network and rank drugs according to their ability to induce apoptosis. We then model the signaling network and mutations of Pancreatic cancer using a multi-fault Boolean framework and simulate the network to theoretically assess the efficacy of drug combinations. Finally, we use a modified Boolean approach to mathematically model feedback loops in Lung cancer and determine the drug combinations that produce cell death for the majority of mutations.
Our theoretical analyses point out that drug combinations containing Cryptotanshinone, a compound found in traditional Chinese herbs, result in significantly increased cell death in each of Breast, Pancreatic, and Lung cancer pathways. We corroborated our theoretical results with experiments on MCF-7 breast cancer cell lines, Human Pancreatic Cancer (HPAC) cell lines, H2073 and SW900 lung cancer cell lines.
Subject
Bayesian modelingBoolean networks
Cancer modeling
Biological pathways
Computational biology
Citation
Vundavilli, Venkata Shirdi (2021). MATHEMATICAL MODELING AND INFERENCE OF CANCER NETWORKS. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /193313.